Anonymized netflow and security scan data

Anonymization Methodology To ensure privacy while retaining the dataset's analytical value, the following anonymization techniques are applied: The main objective is to maintain the utility of network patterns and relationships while masking specific addresses to prevent any form of trace-back to individual devices or networks. IPv4 Address Anonymization Each IPv4 address in the dataset has its first two octets anonymized, using a consistent mapping system that replaces these octets with random, uniquely assigned numbers.

This transformation is deterministic, meaning that the same original address segments always map to the same anonymized segments, thus preserving relationships and patterns critical for analysis. Domain Name Anonymization The hostnames within domain names are anonymized by substituting them with a randomly generated string. These new hostnames follow a structured anonymized format: <randomname>.random.xyz.

Similar to IP anonymization, the mapping is consistent across the dataset, ensuring that each original hostname is consistently replaced with the same anonymized version. Privacy Considerations Consistency: The anonymization process employs a reproducible mapping system, ensuring that every occurrence of a unique IP address segment or domain hostname is anonymized identically across the dataset. This consistency allows for meaningful analysis of trends and repeated interactions without exposing raw data.

Data Integrity: By focusing the anonymization on specific segments of IP addresses and hostnames, the overall structure of the data remains intact. This integrity is crucial for operations such as network flow analysis and anomaly detection, which rely on the continuity of data patterns. Data Minimization: Alongside anonymizing critical fields, the dataset also undergoes a process of column removal, where non-essential fields that might contain sensitive information are excluded.

This further reduces the risk of unintended information exposure.